Written Paper

Use of artificial intelligence techniques for diagnosis of malignant pleural mesothelioma  [2015]

Er, Orhan; Bozok University, Department of Electrical And Electronics Engineering, Yozgat, Turkey Tanrikulu, A. Çetin; Dicle University, Faculty of Medicine, Department Of Chest Diseases, Diyarbakir, Turkey Abakay, Abdurrahman; Dicle University, Faculty of Medicine, Department Of Chest Diseases, Diyarbakir, Turkey

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Objective: Malignant pleural mesothelioma is a highly aggressive tumor of the serous membranes, which in humans results from exposure to asbestos and asbestiform fibers. The incidence of malignant mesothelioma is extremely high in some Turkish villages where there is a low-level environmental exposure to erionite, a fibrous zeolite. Therefore epidemiological studies are difficult to perform in Turkey. Methods: In this paper, a study on malignant pleural mesothelioma disease diagnosis was realized by using artificial immune system. Also, the artificial immune system result was compared with the result of the multi-layer neural network focusing on malignant pleural mesothelioma disease diagnosis and using same database. The malignant pleural mesothelioma disease dataset were prepared from a faculty of medicine’s database using patient’s hospital reports. Results: 97.74% accuracy performance is obtained by artificial immune system. The accuracy results of artificial immune system algorithm are much better than the accuracy results of multi-layer neural network algorithm. Conclusion: This system is capable of conducting the classification process with a good performance to help the expert while deciding the healthy and patient subjects. So, this structure can be helpful as learning based decision support system for contributing to the doctors in their diagnosis decisions. Key words: malignant pleural mesothelioma disease diagnosis, artificial immune system, ma
chine learning based decision support system.


Dicle Tıp Dergisi; Cilt 42, Sayı 1 (2015): Dicle Tıp Dergisi / Dicle Medical Journal

ISSN : 1300-2945